Corpus-scoped annotation and analysis

ABSTRACT

Corpus-scoped annotation and analysis. Enrichment analysis data is generated including annotations and metadata for a plurality of documents that are part of a corpus. Whether to generate a second set of annotations is determined, based on a correlation of the annotations and metadata. A relational database is populated with the enrichment analysis data. A corpus-scoped query is resolved, initiated by an application, using the enrichment analysis data and content of the corpus.

FIELD OF THE INVENTION

The present invention relates generally to the field of contentanalysis, and more particularly analyzing content in a cognitivecomputational environment.

SUMMARY

Embodiments of the present invention provide systems, methods, andcomputer program products for corpus-scoped annotation and analysis.Enrichment analysis data is generated including annotations and metadatafor a plurality of documents that are part of a corpus. Whether togenerate a second set of annotations is determined, based on acorrelation of the annotations and metadata. A relational database ispopulated with the enrichment analysis data. A corpus-scoped query isresolved, initiated by an application, using the enrichment analysisdata and content of the corpus.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a cognitive computing environment, inaccordance with an embodiment of the present invention;

FIG. 2 is a block diagram of generated runtime artifacts using anannotation store, in accordance with an embodiment of the presentinvention;

FIG. 3 is a flowchart illustrating operational steps for creating andstoring enrichment analysis data, in accordance with an embodiment ofthe present invention;

FIG. 4 is a block diagram of internal and external components of thecomputer systems of FIG. 1, in accordance with an embodiment of thepresent invention;

FIG. 5 depicts a cloud computing environment, in accordance with anembodiment of the present invention; and

FIG. 6 depicts abstraction model layers, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

A cognitive computing environment may implement services to generate asearchable corpus from structured, semi-structured, and/or unstructureddata. The corpus may be cognitively processed by analytic engines toprovide knowledge and insights relative to a domain, a topic, a person,or an issue, based on training and observations from all varieties,volumes, and velocities of data. The analytic engines may create modelsto represent the domain and generate and score hypotheses to answerquestions and/or provide further insight. For example, such analyticengines may rely on rule-based learning as well as machine-learning togenerate analysis data for the corpus, whereby the analysis data may beused by applications wishing to perform higher level cognitiveprocessing (e.g., knowledge and insights).

Typical cognitive computing environments handle annotations and otheranalysis data for a corpus at a document granularity, thereby making itdifficult to search and analyze annotations at an entire document-setlevel, or in a corpus-scoped manner. Typically, methods for cognitiveprocessing rely on techniques such as storing the annotated context in auniform annotation structure (e.g., Unstructured Information ManagementArchitecture (UIMA™) Common Analysis System (CAS) Object, JavaScriptObject Notation (JSON) Object, etc.). All terms mentioned in thisspecification that are known to be trademarks or service marks have beenappropriately capitalized. Use of such terms in this specificationshould not be regarded as affecting the validity of the trademark orservice mark. UIMA™ is a trademark of the Apache Software Foundationunder Apache License 2.0. These techniques may provide a completerepresentation of the annotations that exist in documents, and laterindex the documents by keywords to enable a lookup using keywords. Ifthe documents are identified via a keyword search, then the fullannotation structure may be used to retrieve the annotations for theidentified documents.

Although typical cognitive computing environments enable applications tohandle higher level cognitive processing of a corpus and a document-setlevel, the typical cognitive computing environments do not easilyprovide applications with the ability to determine relationships andtrends across the entire corpus. For example, a typical cognitivecomputing environment receive a query, “which concepts frequently occurtogether or are in close proximity?” In this example, the typicalcognitive computing environment may attempt to handle the query byinefficiently analyzing each set of annotated documents within thecorpus to identify detailed annotations, and typically, the index ofkeywords or annotations generated by the analytic engines do not includethe level of detail required for this type of query. Accordingly, it maybe advantageous to create a relational, indexed storage solution forstoring content, metadata and annotations for a corpus in a normalizedmanner such that annotation search and analysis can be performed in acomprehensive and corpus-scoped manner. Furthermore, an instance of sucha storage solution may result in billions of annotations across millionsof documents included in a corpus.

Embodiments of the present invention provide methods, systems, andcomputer program products for generating enrichment analysis dataincluding annotations and metadata for a plurality of documents that arepart of a corpus. Embodiments of the present invention populate arelational database with the enrichment analysis data, wherein therelational database is configured to manage content, participate inincremental ingestion and provide versioning capabilities. Furthermore,embodiments of the present invention resolve a corpus-scoped queryinitiated by an application using the enrichment analysis data andcontent of the corpus.

FIG. 1 is a block diagram of cognitive computing environment 100, inaccordance with an embodiment of the present invention. Cognitivecomputing environment 100 includes storage 110, analytic engine 130,application 140, and content 150, interconnected by network 120. In thisembodiment, components of cognitive computing environment 100 (e.g.,storage 110, analytic engine 130, etc.) are each a part of separatecomputing systems connected by network 120. In another embodiment, aportion of such components may be included in one or more computingsystems, such that any separate components are configured to exchangedata between one another over network 120. Furthermore, such computingsystems may be desktop computers, laptop computers, specialized computerservers, or any other computer system known in the art. In certainembodiments, the computing systems may represent computer systemsutilizing clustered computers and components to act as a single pool ofseamless resources when accessed through network 120. For example, suchembodiments may be used in data center, cloud computing, storage areanetwork (SAN), wide area network (WAN), and network attached storage(NAS) applications. In certain embodiments, the computing systemsrepresent virtual machines. In general, the computer systems describedherein are representative of any electronic device, or combination ofelectronic devices, capable of executing machine-readable programinstructions, in accordance with an embodiment of the present invention,as described in greater detail with regard to FIG. 4. In thisembodiment, the computing systems are implemented in various cloudcomputing environments, as described in greater detail with regard toFIGS. 5 and 6.

Network 120 can be, for example, a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or a combination of the two, andinclude wired, wireless, or fiber optic connections. In general, network120 can be any combination of connections and protocols that willsupport communications between storage 110, analytic engine 130,application 140, and content 150, in accordance with an embodiment ofthe invention.

Storage 110 represents a storage component configured to store a corpusgenerated from content 150 in addition to enrichment analysis data(e.g., annotations and metadata) produced by analytic engine 130, asdescribed in greater detail with regard to FIG. 2. In this embodiment,storage 110 includes a relational database, such as IBM® DB2®, oranother type of relational database management system (RDBMS), storingthe normalized collection of the enrichment analysis data. IBM® DB2® areregistered trademarks of International Business Machines Corporation.Storage 110 leverages the benefit of a relational database by providingfeatures and functions such as, query optimization, filtering,summarization, joins, groupings, derived values, incremental ingestions,updates/deletes, security, reliability, performance, scalability,disaster recovery, and auditability. These features and functions enablecognitive computing environment 100 to manage content 150 at the contentprovider, document, annotator, and annotation level, and furtherprovides the ability to participate in batch and incremental ingestion,as well as provide versioning capabilities. Furthermore, enrichmentanalysis data stored in storage 110 may be used to populate anannotation store from which knowledge graphs, searchable indices, andgraph databases can be generated, as described in FIG. 2.

Analytic engine 130 is a software component configured to process andingest content 150 into a searchable corpus as well as generateenrichment analysis data for the corpus, including annotations andmetadata. For example, analytic engine 130 may validate structured,semi-structured, and unstructured data included in content 150 to ensurethat it is readable, searchable, and comprehensible. Furthermore,analytic engine 130 provides configurable, extensible ingestionworkflows to support natural language processing, text analysis, textmining and content enrichment capabilities. Features such as languageidentification, semantic scoring, lemmatization, clustering andclassification, key phrase extraction, synonym expansion, document andentity sentiment calculation, dictionary-based and/or statistical-basedentity extraction, predictive analytics, and trending entityidentification may be supported by analytic engine 130. In thisembodiment, analytic engine 130 relies on rule-based and machinelearning to analyze, process and ingest content 150. For example,document and entity-sentiment calculation may be performed by analyticengine 130 using machine learning, and predictive analytics may beperformed by analytic engine 130 using classification regression andpairwise correlations (i.e., rule-based).

In this embodiment, analytic engine 130 leverages the above-mentionedfeatures and capabilities to generate annotations and metadata for anentire corpus and then populate an annotation store with the generatedannotations and metadata. For example, analytic engine 130 may prepare(e.g., evaluate, validate, curate, transform and/or enhance) the dataincluded in content 150. Afterwards, analytic engine 130 may produce acollection of vetted data (e.g., ground truth or gold standard data) byinvolving subject matter experts to create resources (e.g., annotationguidelines, types of systems, dictionaries of terms). Additionally, thecollection of vetted data can be produced by pre-annotating the databased on dictionaries provided to analytic engine 130, wherepre-annotating is a process of machine-annotating the data of content150 to the extent possible before a machine-learning model is availableto do so. Pre-annotation can reduce human-annotation labor by replacingsome human annotation creation with mere verification of the correctnessof machine annotation. In this example, after establishing thecollection of vetted data, analytic engine 130 may be used to train analgorithm for automatically adding annotations to large collections ofdocuments, such as collections that include millions of documents.Accordingly, analytic engine 130 uses the trained algorithm as well asthe above-mentioned features (e.g., language identification, semanticscoring, lemmatization, clustering and classification, key phraseextraction, synonym expansion, document and entity sentimentcalculation, dictionary-based and/or statistical-based entityextraction, predictive analytics, and trending entity identification) tocreate annotations for data in content 150.

Content 150 represents structured, semi-structured, and unstructureddata that is provided by one or more content providers and is used tocreate a searchable corpus for analytic engine 130 to later analyze. Inone embodiment, data included in content 150 may be evaluated and thenlater curated, enhanced, and/or transformed by analytic engine 130 priorto ingesting the data into a searchable corpus. For example, content 150may be evaluated to ensure that text-based resources such as journalarticles, textbooks, and research documents are annotated with headingsor tags to assist cognitive computing environment 100 identify andclassify the content in specific documents.

Application 140 represents a software component configured to analyze acorpus and enrichment analysis data (e.g., annotations and metadata) toperform higher level cognitive processing, such as knowledge and insightanalytics relative to a domain, a topic, a person, or an issue.Application 140 interacts with storage 110 to retrieve pertinentinformation that is necessary to answer complex queries and summarizeannotations across many millions of documents included in the corpus. Inone embodiment, application 140 may be used in conjunction with analyticengine 130 to generate and/or regenerate cognitive computing artifactssuch as knowledge graphs, searchable indices, and graph databases, asdescribed in FIG. 2.

The descriptions of the various embodiments of the present inventionhave been presented for the purposes of illustration, but are notintended to be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing form the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

FIG. 2 is a block diagram of storage 110, in accordance with anembodiment of the present invention. In this embodiment, annotationstore 112 is a relational database, such as IBM® DB2®, from which theruntime artifacts including knowledge graph 113, searchable index 114and graph database 115 are generated. In another embodiment, otherhigher-level knowledge artifacts may be generated, such as analysis andscoring algorithms. Annotation store 112 provides the ability forincremental updates, deletes and versioning for enrichment analysis databecause it is a relational database. In contrast, typical cognitivecomputing systems do not implement such relational databases, therebyrequiring a full replacement when an annotator changes. The use of arelational database in storage 110 provides for more efficient filteringoperations. For example, consider a corpus with many generatedannotations that may not be needed for a particular use case. In thisexample, the relational database, or annotation store 112, allows forefficient creation of an instance that only includes annotations neededfor the particular use case, and eliminates the remaining generatedannotations.

Storage 110 relies on a combination of annotation store 112 as well as astored corpus including the full textual content of each document, thuseliminating the need for UIMA™ CAS objects, JSON objects, and/or otherstructures for each document. Furthermore, such objects and/orstructures may be discarded when the annotators are executed andannotation store 112 is populated, thus reducing the storage requirementand save time during deployment. Additionally, annotation store 112 isnot only a representation of the annotators which have been executed,but it may also become an additional source of correlations and insightsthat can provide for new annotations, which may also be populated backinto annotation store 112. Accordingly, such a recursive annotationprocess may be advantageous, because a first analysis is performed withindividual annotators, and then another analysis may be performedagainst the entire corpus.

Knowledge graph 113 represents a knowledge base used to enhancesearchability with semantic-search information gathered from a varietyof sources. For example, application 140 may interact with knowledgegraph 113 to resolve a query without navigating through individualdocuments of the corpus and subsequently assemble the pertinentinformation. Searchable index 114 represents a collection of data, suchas a table or index that is used to achieve efficient and fast searchresponses because, instead of application 140 searching the textdirectly, application 140 searches an index instead. Graph database 115represents a scalable database that uses graph structures for semanticqueries with nodes, edges and proprieties to represent and store data.Furthermore, the graph, or edge or relationship, directly relates dataitems in the store, allowing data in the store to be linked togetherdirectly, and retrieved with one operation. Accordingly, the enrichmentanalysis data stored in storage 110, including the annotations andmetadata which identify the meaning of text, and not just text patterns,in addition to the text content of a corpus provides the ability toperform annotation search and analysis in a comprehensive andcorpus-scoped manner.

FIG. 3 is a flowchart illustrating operational steps for creating andstoring enrichment analysis data, in accordance with an embodiment ofthe present invention. In this embodiment, content 150 includes varioustypes of data used to generate a corpus. Furthermore, analytic engine130 provides tools and services to analyze, process and ingest content150 resulting in the generation of enrichment analysis data that isstored in storage 110.

Analytic engine 130 prepares the data (step 302). In one embodiment,analytic engine 130 may evaluate, validate, curate, transform and/orenhance the data included in content 150 prior to ingestion.Furthermore, analytic engine 130 may rely on rule-based and/or machinelearning techniques to ensure that all relevant terminology, such asentity names, domain vocabulary, and special expressions, areidentifiable prior to ingestion.

After the data is prepared, analytic engine 130 creates annotations andmetadata for the documents included in the generated corpus (step 304).Metadata may include details about the generated annotations or otherword, phrase, concept, and/or document relationships. As previouslydescribed, analytic engine 130 uses trained algorithms, rule-based, andother machine-learning techniques to create annotations for data in thegenerated corpus. For example, a collection of vetted data (e.g., groundtruth or gold standard data) may be created to train an algorithm usedby analytic engine 130. In this example, the use of the trainedalgorithm in addition to other analytic engine 130 features (e.g.,language identification, semantic scoring, lemmatization, clustering andclassification, key phrase extraction, synonym expansion, document andentity sentiment calculation, dictionary-based and/or statistical-basedentity extraction, predictive analytics, and trending entityidentification) enable analytic engine 130 to create annotations andmetadata for the documents included in the generated corpus.Furthermore, analytic engine 130 may support incremental annotation, aswell as support incremental ingestion. In instances where corpus updatesare received, analytic engine 130 may also analyze and process the dataincluded in the corpus updates to create, generate or regenerate,annotators and metadata.

Analytic engine 130 subsequently processes and enriches the annotationsand metadata to generate enrichment analysis data (step 306). Forexample, as previously described, annotation store 112 is not only arepresentation of the annotators which have been executed, but it mayalso become an additional source of correlations and insights that canprovide for new annotations, which may also be populated back intoannotation store 112. Accordingly, such a recursive annotation processperformed during this enrichment phase may be advantageous, because afirst analysis is performed with individual annotators, and then anotheranalysis may be performed against the entire corpus. In another example,enriching the annotations and metadata may performed after receiving anupdated collection of vetted data (e.g., ground truth or gold standarddata) due to, for example, an updated corpus. In this example, if anupdated collection of vetted data is used to re-train an algorithm ofanalytic engine 130, then analytic engine 130 applies the newly trainedalgorithm along with any updated analysis features to generate newannotations and metadata. Accordingly, the new annotations and metadataresults are compared to the previously generated annotations andmetadata, and any identified conflicts are resolved. Adjudication inthis phase is needed to ensure accurate and consistently annotateddocuments included in the enrichment analysis data are promoted toground truth data. In one embodiment, annotator updates may be receivedduring enrichment, whereby an incremental annotation process may beinitiated.

Once the enrichment analysis data is generated, then it is stored instorage 112 (step 308). As previously described, annotations andmetadata that are part of the enrichment analysis data may be stored ina relational database, such as annotation store 112, thereby providingapplication 140 with the ability to search and analyze annotations at acorpus-scoped manner. Once enrichment analysis data is stored in arelational database, cognitive computing environment 100 is configuredto receive a corpus-scoped query, optimize the corpus-scoped query usingquery optimizations features of a relational database management system,and resolve the corpus-scoped query using the enrichment analysis datathat described annotations and metadata for the entire corpus. The useof relational databases enables for the generation of additional runtimeartifacts, such as knowledge graphs, searchable indices, and graphdatabases, which may be used by analytic engine 130 to resolve thecorpus-scoped query.

FIG. 4 is a block diagram of internal and external components of acomputer system 400, which is representative the computer systems ofFIG. 1, in accordance with an embodiment of the present invention. Itshould be appreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Ingeneral, the components illustrated in FIG. 4 are representative of anyelectronic device capable of executing machine-readable programinstructions. Examples of computer systems, environments, and/orconfigurations that may be represented by the components illustrated inFIG. 4 include, but are not limited to, personal computer systems,server computer systems, thin clients, thick clients, laptop computersystems, tablet computer systems, cellular telephones (e.g., smartphones), multiprocessor systems, microprocessor-based systems, networkPCs, minicomputer systems, mainframe computer systems, and distributedcognitive computing environments that include any of the above systemsor devices.

Computer system 400 includes communications fabric 402, which providesfor communications between one or more processors 404, memory 406,persistent storage 408, communications unit 412, and one or moreinput/output (I/O) interfaces 414. Communications fabric 402 can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system. For example,communications fabric 402 can be implemented with one or more buses.

Memory 406 and persistent storage 408 are computer-readable storagemedia. In this embodiment, memory 406 includes random access memory(RAM) 416 and cache memory 418. In general, memory 406 can include anysuitable volatile or non-volatile computer-readable storage media.Software is stored in persistent storage 408 for execution and/or accessby one or more of the respective processors 404 via one or more memoriesof memory 406.

Persistent storage 408 may include, for example, a plurality of magnetichard disk drives. Alternatively, or in addition to magnetic hard diskdrives, persistent storage 408 can include one or more solid state harddrives, semiconductor storage devices, read-only memories (ROM),erasable programmable read-only memories (EPROM), flash memories, or anyother computer-readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 408 can also be removable. Forexample, a removable hard drive can be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage408.

Communications unit 412 provides for communications with other computersystems or devices via a network (e.g., network 120). In this exemplaryembodiment, communications unit 412 includes network adapters orinterfaces such as a TCP/IP adapter cards, wireless Wi-Fi interfacecards, or 3G or 4G wireless interface cards or other wired or wirelesscommunication links. The network can comprise, for example, copperwires, optical fibers, wireless transmission, routers, firewalls,switches, gateway computers and/or edge servers. Software and data usedto practice embodiments of the present invention can be downloadedthrough communications unit 412 (e.g., via the Internet, a local areanetwork or other wide area network). From communications unit 412, thesoftware and data can be loaded onto persistent storage 408.

One or more I/O interfaces 414 allow for input and output of data withother devices that may be connected to computer system 400. For example,I/O interface 414 can provide a connection to one or more externaldevices 420, such as a keyboard, computer mouse, touch screen, virtualkeyboard, touch pad, pointing device, or other human interface devices.External devices 420 can also include portable computer-readable storagemedia such as, for example, thumb drives, portable optical or magneticdisks, and memory cards. I/O interface 414 also connects to display 422.

Display 422 provides a mechanism to display data to a user and can be,for example, a computer monitor. Display 422 can also be an incorporateddisplay and may function as a touch screen, such as a built-in displayof a tablet computer.

Referring now to FIG. 5, illustrative cognitive computing environment 50is depicted. As shown, cognitive computing environment 50 comprises oneor more cloud computing nodes 10 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 54A, desktop computer 54B, laptop computer54C, and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cognitive computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. The types of computing devices 54A-N shown in FIG. 4 areintended to be illustrative only and that cloud computing nodes 10 andcognitive computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cognitive computing environment 50 (FIG. 5) is shown. The components,layers, and functions shown in FIG. 6 are intended to be illustrativeonly and embodiments of the invention are not limited thereto. Asdepicted, the following layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cognitive computing environment. Metering and Pricing82 provide cost tracking as resources are utilized within the cognitivecomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cognitive computing environmentfor consumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecognitive computing environment may be utilized. Examples of workloadsand functions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and cognitive computing environment 96.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cognitive computing environment.Rather, embodiments of the present invention are capable of beingimplemented in conjunction with any other type of computing environmentnow known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds). A cognitive computing environment isservice oriented with a focus on statelessness, low coupling,modularity, and semantic interoperability. At the heart of cloudcomputing is an infrastructure comprising a network of interconnectednodes.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A method comprising: producing a collection ofprepared data, from structured and unstructured data, utilizingrule-based and/or machine learning techniques, wherein the structuredand unstructured data is provided by a content provider; producing acollection of vetted data, based on the collection of prepared data,utilizing a subject matter expert to create a dictionary of terms;creating a trained algorithm based on the collection of vetted data;utilizing the trained algorithm in combination with at least one featurefrom the group consisting of: language identification, semantic scoring,lemmatization, clustering and classification, key phrase extraction,synonym expansion, document and entity sentiment calculation, predictiveanalytics, and trending entity identification to generate, by one ormore computer processors, enrichment analysis data, wherein theenrichment analysis data comprises a first set of annotations andmetadata for a first plurality of documents that are part of a corpus;determining, by the one or more computer processors, whether to generatea second set of annotations and metadata for a second plurality ofdocuments that are part of the corpus, based on a correlation of thefirst set of annotations and metadata; determining, by the one or morecomputer processors, whether to generate the second set of annotationsand metadata, utilizing a re-trained algorithm, wherein the re-trainedalgorithm is created in response to updates to the collection of vetteddata; generating, by the one or more computer processors, updatedenrichment analysis data, wherein the updated enrichment analysis datacomprises the first set of annotations and metadata combined with thesecond set of annotations and metadata; populating, by the one or morecomputer processors, a relational database with the enrichment analysisdata, wherein the relational database is configured to manage the entiretext content of the documents that are part of the corpus andparticipate in incremental ingestion during population of the relationaldatabase with enrichment analysis data, and wherein the relationaldatabase is configured to provide versioning capabilities for theenrichment analysis data stored in the relational database; populating,by the one or more computer processors, the relational database with theupdated enrichment analysis data; generating, by the one or morecomputer processors, one or more runtime artifacts, wherein generatingthe one or more runtime artifacts comprises a first analysis using theenrichment analysis data against the first plurality of documents and asecond analysis using the updated enrichment analysis data against thesecond plurality of documents; and resolving, by the one or morecomputer processors, a corpus-scoped query initiated by an applicationusing the one or more runtime artifacts, wherein resolving thecorpus-scoped query comprises optimizing, by the one or more computerprocessors, the corpus-scoped query through a query optimization featureof a relational database management system for the relational database.